A hybrid approach to vehicle routing using neural networks and genetic algorithms

A competitive neural network model and a genetic algorithm are used to improve the initialization and construction phase of a parallel insertion heuristic for the vehicle routing problem with time windows. The neural network identifies seed customers that are distributed over the entire geographic area during the initialization phase, while the genetic algorithm finds good parameter settings in the route construction phase that follows. Computational results on a standard set of problems are also reported.     

Evolving dynamic Bayesian networks with Multi-objective genetic algorithms

A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network's probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a basic structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favors sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promising results when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA. Brian J. Ross is a professor of computer science at Brock University, where he has worked since 1992. He obtained his BCSc at the University of Manitoba, Canada, in 1984, his M.Sc. at the University of British Columbia, Canada, in 1988, and his Ph.D. at the University of Edinburgh, Scotland, in 1992. His research interests include evolutionary computation, language induction, concurrency, and logic programming. He is also interested in computer applications in the fine arts. Eduardo Zuviria received a BS degree in Computer Science from Brock University in 2004 and a MS degree in Computer Science from Queen's University in 2006 where he held jobs as teacher and research assistant. Currently, he is attending a Ph.D. program at the University of Montreal. He holds a diploma in electronics from a technical college and has worked for eight years in the computer industry as a software developer and systems administrator. He has received several scholarships including the Ontario Graduate Scholarship, Queen's Graduate Scholarship and a NSERC- USRA scholarship.     

Forecasting nonlinear time series with neural network sieve bootstrap

A new method to construct nonparametric prediction intervals for nonlinear time series data is proposed. Within the framework of the recently developed sieve bootstrap, the new approach employs neural network models to approximate the original nonlinear process. The method is flexible and easy to implement as a standard residual bootstrap scheme while retaining the advantage of being a nonparametric technique. It is model-free within a general class of nonlinear processes and avoids the specification of a finite dimensional model for the data generating process. The results of a Monte Carlo study are reported in order to investigate the finite sample performances of the proposed procedure.     

Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

用改进的遗传算法训练神经网络构造分类器

针对基本遗传算法存在容易早熟和局部搜索能力弱等缺陷,提出了改进的遗传算法,引入交叉概率和变异概率与个体的适度值相联系,改进了操作算子,而且在交叉操作后又引入模拟退火机制,提高遗传算法的局部搜索能力。同时,用改进的遗传算法和基本的遗传算法训练神经网络构造分类器,实验结果表明,改进的遗传算法在最好个体适度值和最好分类准确性等方面性能更好。     

Fuzzy lagged variable selection in fuzzy time series with genetic algorithms

Fuzzy time series forecasting models can be divided into two subclasses which are first order and high order. In high order models, all lagged variables exist in the model according to the model order. Thus, some of these can exist in the model although these lagged variables are not significant in explaining fuzzy relationships. If such lagged variables can be removed from the model, fuzzy relationships will be defined better and it will cause more accurate forecasting results. In this study, a new fuzzy time series forecasting model has been proposed by defining a partial high order fuzzy time series forecasting model in which the selection of fuzzy lagged variables is done by using genetic algorithms. The proposed method is applied to some real life time series and obtained results are compared with those obtained from other methods available in the literature. It is shown that the proposed method has high forecasting accuracy.     

基于遗传算法和梯度下降的RBF神经网络组合训练方法

在使用基于梯度下降的径向基函数（RBF）神经网络学习方法时，由于网络目标函数误差曲面极其复杂，因而产生了网络收敛速度慢，且容易陷入局部极小，网络初始值的设置对网络训练结果影响很大等问题。基于遗传算法的训练方法能够摆脱陷入局部最优的困扰，但遗传算法的局部搜索能力不够，从而影响网络的训练效果。为了解决上述问题，在研究两种算法特点的基础上，提出一种组合训练方法，用提出的训练方法对UCI中的部分数据集进行了仿真实验，并将实验结果与传统方法下的结果进行了比较，实验结果表明新方法是有效的。     

人工神经网络与改进遗传算法的协作求解

简要介绍了改进遗传算法求解问题的步骤以及解决实际问题的特点。为了利用改进遗传算法的优点,提高其收敛速度,提出改进遗传算法与人工神经网络（BP网络）利用神经网络的联想记忆、特征提取功能辅助遗传算法求解结构优化设计问题,以避免在遗传算法中所作的那些不必要的分析计算,从而节省了计算时间。最后通过算例证实,比简单遗传算法与人工神经网络协作计算时间减少约25%。     

改进遗传神经网络控制混沌运动的研究

用最大Lyapunov指数构造遗传算法中的适应度函数,通过遗传算法优化神经网络的权系数．根据所得到的适应度函数和权系数来构造遗传神经网络控制器,从而提高神经网络控制效果．对离散系统Logistic映射和连续系统Rossler方程、AFM（原子力显微镜）悬臂梁振动系统的混沌运动分别进行了仿真控制．数值实验结果表明本文改进的遗传神经网络控制方法对离散或者连续的混沌系统都能控制到低周期轨道上去,证明了算法的有效性．     

基于时空图卷积网络的多变量时间序列预测方法

为了扩大时空图卷积网络的预测范围,将它应用在关联关系未知场景下的多变量时间序列预测问题,提出一种附加图学习层的时空图卷积网络预测方法（GLB-STGCN）。图学习层借助余弦相似度从时间序列中学习图邻接矩阵,通过图卷积网络捕捉多变量之间的相互影响,最后通过多核时间卷积网络捕捉时间序列的周期性特征,实现对多变量的精准预测。为验证GLB-STGCN的有效性,使用天文、电力、交通和经济四个领域的公共数据集和一个工业场景生产数据集进行预测实验,结果表明GLB-STGCN优于对比方法,在天文数据集上的表现尤为出色,预测误差分别降低了6.02%、8.01%、6.72%和5.31%。实验结果证明GLB-STGCN适用范围更广,预测效果更好,尤其适合自然周期明显的时间序列预测问题。     

A new approach for automated parts recognition using time series analysisand neural networks

This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.     

The application of ridge polynomial neural network to multi-step ahead financial time series prediction

Motivated by the slow learning properties of multilayer perceptrons (MLPs) which utilize computationally intensive training algorithms, such as the backpropagation learning algorithm, and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN), which maintain fast learning properties and powerful mapping capabilities of single layer high order neural networks. The RPNN is constructed from a number of increasing orders of Pi–Sigma units, which are used to capture the underlying patterns in financial time series signals and to predict future trends in the financial market. In particular, this paper systematically investigates a method of pre-processing the financial signals in order to reduce the influence of their trends. The performance of the networks is benchmarked against the performance of MLPs, functional link neural networks (FLNN), and Pi–Sigma neural networks (PSNN). Simulation results clearly demonstrate that RPNNs generate higher profit returns with fast convergence on various noisy financial signals.     

Multiple neural networks for a long term time series forecast

The artificial neural network (ANN) methodology has been used in various time series prediction applications. However, the accuracy of a neural network model may be seriously compromised when it is used recursively for making long-term multi-step predictions. This study presents a method using multiple ANNs to make a long term time series prediction. A multiple neural network (MNN) model is a group of neural networks that work together to solve a problem. In the proposed MNN approach, each component neural network makes forecasts at a different length of time ahead. The MNN method was applied to the problem of forecasting an hourly customer demand for gas at a compression station in Saskatchewan, Canada. The results showed that a MNN model performed better than a single ANN model for long term prediction.

动态非线性连续时间系统的小波神经网络辨识

将小波神经网络应用于动态非线性连续时间系统的辨识, 同时为了使神经网络的训练达到全局最优和加速小波神经网络训练的收敛速度, 提出了信赖域算法, 并研究了信赖域算法的收敛性. 随后进行了算例仿真, 证明了所提辨识方法的有效性.     

Financial time series prediction using polynomial pipelined neural networks

This paper proposes a novel type of higher-order pipelined neural network: the polynomial pipelined neural network. The proposed network is constructed from a number of higher-order neural networks concatenated with each other to predict highly nonlinear and nonstationary signals based on the engineering concept of divide and conquer. The polynomial pipelined neural network is used to predict the exchange rate between the US dollar and three other currencies. In this application, two sets of experiments are carried out. In the first set, the input data are pre-processed between 0 and 1 and passed to the neural networks as nonstationary data. In the second set of experiments, the nonstationary input signals are transformed into one step relative increase in price. The network demonstrates more accurate forecasting and an improvement in the signal to noise ratio over a number of benchmarked neural networks.     

Efficient algorithms for segmentation of item-set time series

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets–Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.     

Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach

A critical aspect of wire bonding is the quality of the bonding strength that contributes the major part of yield loss to the integrated circuit assembly process. This paper applies an integrated approach using a neural networks and genetic algorithms to optimize IC wire bonding process. We first use a back-propagation network to provide the nonlinear relationship between factors and the response based on the experimental data from a semiconductor manufacturing company in Taiwan. Then, a genetic algorithms is applied to obtain the optimal factor settings. A comparison between the proposed approach and the Taguchi method was also conducted. The results demonstrate the superiority of the proposed approach in terms of process capability.     

基于改进遗传算法的多示例神经网络优化

多示例神经网络是一类用于求解多示例学习问题的神经网络,但由于其中有不可微函数,使用反向传播训练方法时需要采用近似方法,因此多示例神经网络的预测准确性不高。〖BP）〗为了提高预测准确性,构造了一类优化多示例神经网络参数的改进遗传算法, 借助基于反向传播训练的局部搜索算子、排挤操作和适应性操作概率计算方式来提高收敛速度和防止早熟收敛。通过公认的数据集上实验结果的分析和对比,证实了这个改进的遗传算法能够明显地提高多示例神经网络的预测准确性,同时还具有比其他算法更快的收敛速度。     

Evolving rule induction algorithms with multi-objective grammar-based genetic programming

Multi-objective optimization has played a major role in solving problems where two or more conflicting objectives need to be simultaneously optimized. This paper presents a Multi-Objective grammar-based genetic programming (MOGGP) system that automatically evolves complete rule induction algorithms, which in turn produce both accurate and compact rule models. The system was compared with a single objective GGP and three other rule induction algorithms. In total, 20 UCI data sets were used to generate and test generic rule induction algorithms, which can be now applied to any classification data set. Experiments showed that, in general, the proposed MOGGP finds rule induction algorithms with competitive predictive accuracies and more compact models than the algorithms it was compared with.

基于混合递阶遗传算法的径向基神经网络学习算法及其应用

在研究径向基神经网络学习算法的基础上, 提出了一种新型的径向基神经网络学习算法———混合递阶遗传算法. 该算法将递阶遗传算法和最小二乘法的优点结合在一起, 能够同时确定径向基神经网络的结构和参数, 并具有较高的学习效率. 采用基于混合递阶遗传算法的径向基神经网络对混沌时间序列学习和预测, 取得了较好的效果.